{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# HSE Введение в машинное обучение\n",
    "\n",
    "## Лекция 2\n",
    "\n",
    "### Polina Polunina\n",
    "polina.polunina@skolkovotech.ru\n",
    "\n",
    "# 1. Linear Regression and Classification Tutorial\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import packages:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#linear algebra\n",
    "import numpy as np\n",
    "#data structures\n",
    "import pandas as pd\n",
    "#ml models\n",
    "import scipy as sp\n",
    "import sklearn\n",
    "from sklearn import datasets\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.svm import SVR\n",
    "#plots\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "#beautiful plots\n",
    "import seaborn as sns\n",
    "#linear regression\n",
    "import statsmodels.api as sm\n",
    "#set style for plots\n",
    "sns.set_style('darkgrid')\n",
    "#off the warnings\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1 Simple Linear Regression\n",
    "Simple Linear Regression - Модель парной регрессии - регрессия с одной переменной"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Подгружаем датасет:\n",
    "* **SalePrice** - The property's sale price in dollars. Это целевая переменная (зависимая переменная), которую мы будем пытаться предсказать\n",
    "* **GrLivArea** - Above grade (ground) living area square feet - Это независимая переменная (предиктор)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('train.csv', index_col = 0, usecols=['Id', 'GrLivArea', 'SalePrice'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Давайте посмотрим на наши данные:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1710</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1262</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1786</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1717</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2198</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    GrLivArea  SalePrice\n",
       "Id                      \n",
       "1        1710     208500\n",
       "2        1262     181500\n",
       "3        1786     223500\n",
       "4        1717     140000\n",
       "5        2198     250000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1515.463699</td>\n",
       "      <td>180921.195890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>525.480383</td>\n",
       "      <td>79442.502883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>334.000000</td>\n",
       "      <td>34900.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1129.500000</td>\n",
       "      <td>129975.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1464.000000</td>\n",
       "      <td>163000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1776.750000</td>\n",
       "      <td>214000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5642.000000</td>\n",
       "      <td>755000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         GrLivArea      SalePrice\n",
       "count  1460.000000    1460.000000\n",
       "mean   1515.463699  180921.195890\n",
       "std     525.480383   79442.502883\n",
       "min     334.000000   34900.000000\n",
       "25%    1129.500000  129975.000000\n",
       "50%    1464.000000  163000.000000\n",
       "75%    1776.750000  214000.000000\n",
       "max    5642.000000  755000.000000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e668c80e80>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.GrLivArea.hist(bins=20, label='GrLivArea')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e668f90a20>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.SalePrice.hist(bins=30, label='SalePrice')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "326099.99999999994"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.SalePrice.quantile(0.95)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e669066080>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#set figsize of the plot\n",
    "plt.figure(figsize = (10,7))\n",
    "#scatter plot of the data\n",
    "plt.scatter(data.GrLivArea, data.SalePrice)\n",
    "#text for x axis\n",
    "plt.xlabel('Living area, square feet')\n",
    "#text for y axis\n",
    "plt.ylabel('Sale Price, dollars')\n",
    "#text for the plot title\n",
    "plt.title('Dependence between House Sale Price and Living Area')\n",
    "#show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Как смоделировать эту зависимость?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Построение модели\n",
    "\n",
    "* Y = SalePrice - целевая, зависимая переменная\n",
    "* X = GrLivArea - предиктор, независимая переменная\n",
    "\n",
    "**Модель**\n",
    "\n",
    "Мы хотим найти прямую, которая наилучшим образом оторбражает зависимость между Sale Price и Living Area\n",
    "\n",
    "$Y = a + bX + \\epsilon$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.GrLivArea\n",
    "Y = data.SalePrice\n",
    "#add the constant term to the data\n",
    "X = sm.add_constant(X)\n",
    "#define the model\n",
    "model = sm.OLS(Y, X)\n",
    "#fit the model\n",
    "results = model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td>Model:</td>               <td>OLS</td>         <td>Adj. R-squared:</td>      <td>0.502</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Dependent Variable:</td>     <td>SalePrice</td>           <td>AIC:</td>         <td>36073.7610</td>\n",
       "</tr>\n",
       "<tr>\n",
       "         <td>Date:</td>        <td>2019-04-10 20:02</td>        <td>BIC:</td>         <td>36084.3334</td>\n",
       "</tr>\n",
       "<tr>\n",
       "   <td>No. Observations:</td>        <td>1460</td>         <td>Log-Likelihood:</td>     <td>-18035.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "       <td>Df Model:</td>              <td>1</td>           <td>F-statistic:</td>        <td>1471.</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Df Residuals:</td>          <td>1458</td>       <td>Prob (F-statistic):</td>  <td>4.52e-223</td>\n",
       "</tr>\n",
       "<tr>\n",
       "      <td>R-squared:</td>            <td>0.502</td>            <td>Scale:</td>        <td>3.1442e+09</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>Coef.</th>   <th>Std.Err.</th>     <th>t</th>     <th>P>|t|</th>  <th>[0.025</th>     <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>     <td>18569.0259</td> <td>4480.7545</td> <td>4.1442</td>  <td>0.0000</td> <td>9779.6119</td> <td>27358.4399</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>GrLivArea</th>  <td>107.1304</td>   <td>2.7936</td>   <td>38.3482</td> <td>0.0000</td> <td>101.6504</td>   <td>112.6103</td> \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td>Omnibus:</td>    <td>261.166</td>  <td>Durbin-Watson:</td>     <td>2.025</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Prob(Omnibus):</td>  <td>0.000</td>  <td>Jarque-Bera (JB):</td> <td>3432.287</td>\n",
       "</tr>\n",
       "<tr>\n",
       "       <td>Skew:</td>      <td>0.410</td>      <td>Prob(JB):</td>       <td>0.000</td> \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Kurtosis:</td>   <td>10.467</td>   <td>Condition No.:</td>     <td>4897</td>  \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                  Results: Ordinary least squares\n",
       "===================================================================\n",
       "Model:              OLS              Adj. R-squared:     0.502     \n",
       "Dependent Variable: SalePrice        AIC:                36073.7610\n",
       "Date:               2019-04-10 20:02 BIC:                36084.3334\n",
       "No. Observations:   1460             Log-Likelihood:     -18035.   \n",
       "Df Model:           1                F-statistic:        1471.     \n",
       "Df Residuals:       1458             Prob (F-statistic): 4.52e-223 \n",
       "R-squared:          0.502            Scale:              3.1442e+09\n",
       "-------------------------------------------------------------------\n",
       "             Coef.     Std.Err.    t    P>|t|    [0.025    0.975]  \n",
       "-------------------------------------------------------------------\n",
       "const      18569.0259 4480.7545  4.1442 0.0000 9779.6119 27358.4399\n",
       "GrLivArea    107.1304    2.7936 38.3482 0.0000  101.6504   112.6103\n",
       "-------------------------------------------------------------------\n",
       "Omnibus:             261.166       Durbin-Watson:          2.025   \n",
       "Prob(Omnibus):       0.000         Jarque-Bera (JB):       3432.287\n",
       "Skew:                0.410         Prob(JB):               0.000   \n",
       "Kurtosis:            10.467        Condition No.:          4897    \n",
       "===================================================================\n",
       "* The condition number is large (5e+03). This might indicate\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#plot the summary of our model\n",
    "results.summary2()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Что значат все эти статистики?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **const** - найденное значение для а\n",
    "* **GrLivArea** - найденное значение для b\n",
    "\n",
    "таким образом, наша модель **Y = 18569.0259 + 107.1304 * X**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Тестирование гипотез и *p-value***\n",
    "\n",
    "$H_0$: coeff = 0 - нулевая гипотеза\n",
    "\n",
    "$H_1$: coeff $\\neq$ 0 - альтернативная гипотеза\n",
    "\n",
    "* Если *p-value* $\\leq$ alpha, тогда мы **МОЖЕМ** отклонить нулевую гипотезу и соответствующий коэффициент зовется **значимым**\n",
    "* Если *p-value* > alpha, то мы **НЕ МОЖЕМ** отклонить нулевую гипотезу и соответствующий коэффициент зовется **незначимым**\n",
    "    \n",
    "    \n",
    "**Как подобрать подходящее значения для alpha?**\n",
    "\n",
    "Значения alpha могут быть... : 0.01, 0.05, 0.1 ... \n",
    "\n",
    "**Напоминание из курса статистики: ошибки 1 и 2 рода:**\n",
    "\n",
    "<img style=\"float: left;\" src=\"pic1.jpg\" br>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img style=\"float: left;\" src=\"pic2.jpg\" br>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Наиболее популярный выбор alpha = 0.05"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Как хорошо найденная функция описывает реальную зависимость?\n",
    "**Y = 18569.0259 + 107.1304 * X**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e6693b4748>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#set size of the plot\n",
    "plt.figure(figsize = (10,7))\n",
    "#scatter plot of the data\n",
    "plt.scatter(data.GrLivArea, data.SalePrice, alpha=0.6, label = 'Original data')\n",
    "#plot of the found regression line\n",
    "plt.plot(data.GrLivArea.values, 18569.0259 + 107.1304 * data.GrLivArea.values, color = 'orchid', label='Model')\n",
    "#text for x axis\n",
    "plt.xlabel('Living area, square feet')\n",
    "#text for y axis\n",
    "plt.ylabel('Sale Price, dollars')\n",
    "#text for the plot title\n",
    "plt.title('Dependence between House Sale Price and Living Area')\n",
    "plt.legend()\n",
    "#show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Оценка подгонки регрессии и $R^2$ \n",
    "\n",
    "Очередное напоминание из вашего курса статистики =)\n",
    "\n",
    "$R^2$ - это **коэффициент детерминации**, одна из наиболее популярных метрик для задачи регрессии, в том числе, для линейной регрессии\n",
    "\n",
    "В случае линейной регрессии, $R^2$ определяется следующим образом:\n",
    "\n",
    "* $y_i$ - наблюдаемые значения целевой переменной\n",
    "* $\\hat{y_i}$ - предсказываемые моделью значения целевой переменной\n",
    "* $\\overline{y} = \\frac{1}{n}\\sum_{i=1}^{n}y_i$ - среднее по наблюдаемым значениям целевой переменной\n",
    "* $SS_{tot} = \\sum_{i}(y_i - \\overline{y})^2$ - общая сумма квадратов (total sum of squares, TSS)\n",
    "* $SS_{reg} = \\sum_{i}(\\hat{y_i} - \\overline{y})^2$ - explained sum of squares, ESS\n",
    "* $SS_{res} = \\sum_{i}(y_i - \\hat{y_i})^2 = \\sum_{i}residual_i^2$ - residual sum of squares, RSS\n",
    "\n",
    "$R^2 = \\frac{SS_{reg}}{SS_{tot}} = 1 - \\frac{SS_{res}}{SS_{tot}}$ - доля объясненной дисперсии (ratio of the explained variance)\n",
    "\n",
    "$R^2$ лежит в интервале [0, 1]\n",
    "\n",
    "В нашей модели $R^2 = 0.502$, т.е. только половина от дисперсии объяснена "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** $R^2$ нестабилен, т.к. увеличивается вместе с добавлением новых данных, поэтому лучше смотреть на $R^2_{adjusted}$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Попробуем сделать лучше?\n",
    "\n",
    "Давайте возьмем логарифмы от X и Y. Тогда модель будет выглядеть так: **$ln(Y) = a + b*ln(X) + \\epsilon$**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.GrLivArea\n",
    "Y = data.SalePrice\n",
    "X=np.log(X)\n",
    "Y=np.log(Y)\n",
    "#add the constant term to the data\n",
    "X = sm.add_constant(X)\n",
    "#define the model\n",
    "model = sm.OLS(Y, X)\n",
    "#fit the model\n",
    "results = model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td>Model:</td>               <td>OLS</td>         <td>Adj. R-squared:</td>     <td>0.533</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Dependent Variable:</td>     <td>SalePrice</td>           <td>AIC:</td>         <td>354.1941</td> \n",
       "</tr>\n",
       "<tr>\n",
       "         <td>Date:</td>        <td>2019-04-10 20:02</td>        <td>BIC:</td>         <td>364.7664</td> \n",
       "</tr>\n",
       "<tr>\n",
       "   <td>No. Observations:</td>        <td>1460</td>         <td>Log-Likelihood:</td>    <td>-175.10</td> \n",
       "</tr>\n",
       "<tr>\n",
       "       <td>Df Model:</td>              <td>1</td>           <td>F-statistic:</td>       <td>1666.</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Df Residuals:</td>          <td>1458</td>       <td>Prob (F-statistic):</td> <td>1.60e-243</td>\n",
       "</tr>\n",
       "<tr>\n",
       "      <td>R-squared:</td>            <td>0.533</td>            <td>Scale:</td>        <td>0.074523</td> \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>       <th>Coef.</th> <th>Std.Err.</th>    <th>t</th>     <th>P>|t|</th> <th>[0.025</th> <th>0.975]</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>     <td>5.6681</td>  <td>0.1559</td>  <td>36.3601</td> <td>0.0000</td> <td>5.3623</td> <td>5.9739</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>GrLivArea</th> <td>0.8745</td>  <td>0.0214</td>  <td>40.8151</td> <td>0.0000</td> <td>0.8325</td> <td>0.9166</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td>Omnibus:</td>    <td>111.954</td>  <td>Durbin-Watson:</td>    <td>2.022</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Prob(Omnibus):</td>  <td>0.000</td>  <td>Jarque-Bera (JB):</td> <td>184.755</td>\n",
       "</tr>\n",
       "<tr>\n",
       "       <td>Skew:</td>     <td>-0.565</td>      <td>Prob(JB):</td>      <td>0.000</td> \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Kurtosis:</td>    <td>4.327</td>   <td>Condition No.:</td>     <td>162</td>  \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                 Results: Ordinary least squares\n",
       "==================================================================\n",
       "Model:              OLS              Adj. R-squared:     0.533    \n",
       "Dependent Variable: SalePrice        AIC:                354.1941 \n",
       "Date:               2019-04-10 20:02 BIC:                364.7664 \n",
       "No. Observations:   1460             Log-Likelihood:     -175.10  \n",
       "Df Model:           1                F-statistic:        1666.    \n",
       "Df Residuals:       1458             Prob (F-statistic): 1.60e-243\n",
       "R-squared:          0.533            Scale:              0.074523 \n",
       "--------------------------------------------------------------------\n",
       "              Coef.    Std.Err.      t      P>|t|    [0.025   0.975]\n",
       "--------------------------------------------------------------------\n",
       "const         5.6681     0.1559   36.3601   0.0000   5.3623   5.9739\n",
       "GrLivArea     0.8745     0.0214   40.8151   0.0000   0.8325   0.9166\n",
       "------------------------------------------------------------------\n",
       "Omnibus:             111.954       Durbin-Watson:          2.022  \n",
       "Prob(Omnibus):       0.000         Jarque-Bera (JB):       184.755\n",
       "Skew:                -0.565        Prob(JB):               0.000  \n",
       "Kurtosis:            4.327         Condition No.:          162    \n",
       "==================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#plot the summary of our model\n",
    "results.summary2()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e669161f28>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#set size of the plot\n",
    "plt.figure(figsize = (10,7))\n",
    "#scatter plot of the data\n",
    "plt.scatter(np.log(data.GrLivArea), np.log(data.SalePrice), alpha=0.6, label = 'Original data')\n",
    "#plot of the found regression line\n",
    "plt.plot(np.log(data.GrLivArea.values), 5.6681 + 0.8745 * np.log(data.GrLivArea.values), color = 'orchid', label='Model')\n",
    "#text for x axis\n",
    "plt.xlabel('log(Living area), square feet')\n",
    "#text for y axis\n",
    "plt.ylabel('log(Sale Price), dollars')\n",
    "#text for the plot title\n",
    "plt.title('Dependence between log(House Sale Price) and log(Living Area)')\n",
    "plt.legend()\n",
    "#show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2 Multiple Linear Regression (Модель множественной линейной регрессии)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols=['Id', 'MSSubClass', 'LotArea', 'OverallQual',\\\n",
    "      'OverallCond', 'YearBuilt', 'YearRemodAdd',\\\n",
    "      'BsmtUnfSF', 'TotalBsmtSF', '1stFlrSF', '2ndFlrSF',\\\n",
    "     'LowQualFinSF', 'GrLivArea', 'BsmtFullBath',\\\n",
    "     'FullBath', 'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr',\\\n",
    "     'TotRmsAbvGrd', 'Fireplaces', 'GarageCars',\\\n",
    "     'GarageArea', 'WoodDeckSF', 'OpenPorchSF', 'EnclosedPorch', '3SsnPorch',\\\n",
    "     'ScreenPorch', 'PoolArea', 'MiscVal', 'MoSold', 'YrSold', 'SalePrice']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <th>...</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>60</td>\n",
       "      <td>8450</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2003</td>\n",
       "      <td>2003</td>\n",
       "      <td>150</td>\n",
       "      <td>856</td>\n",
       "      <td>856</td>\n",
       "      <td>854</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>9600</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>1976</td>\n",
       "      <td>1976</td>\n",
       "      <td>284</td>\n",
       "      <td>1262</td>\n",
       "      <td>1262</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>298</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60</td>\n",
       "      <td>11250</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2001</td>\n",
       "      <td>2002</td>\n",
       "      <td>434</td>\n",
       "      <td>920</td>\n",
       "      <td>920</td>\n",
       "      <td>866</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>70</td>\n",
       "      <td>9550</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1915</td>\n",
       "      <td>1970</td>\n",
       "      <td>540</td>\n",
       "      <td>756</td>\n",
       "      <td>961</td>\n",
       "      <td>756</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>272</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>60</td>\n",
       "      <td>14260</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>490</td>\n",
       "      <td>1145</td>\n",
       "      <td>1145</td>\n",
       "      <td>1053</td>\n",
       "      <td>...</td>\n",
       "      <td>192</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    MSSubClass  LotArea  OverallQual  OverallCond  YearBuilt  YearRemodAdd  \\\n",
       "Id                                                                           \n",
       "1           60     8450            7            5       2003          2003   \n",
       "2           20     9600            6            8       1976          1976   \n",
       "3           60    11250            7            5       2001          2002   \n",
       "4           70     9550            7            5       1915          1970   \n",
       "5           60    14260            8            5       2000          2000   \n",
       "\n",
       "    BsmtUnfSF  TotalBsmtSF  1stFlrSF  2ndFlrSF  ...  WoodDeckSF  OpenPorchSF  \\\n",
       "Id                                              ...                            \n",
       "1         150          856       856       854  ...           0           61   \n",
       "2         284         1262      1262         0  ...         298            0   \n",
       "3         434          920       920       866  ...           0           42   \n",
       "4         540          756       961       756  ...           0           35   \n",
       "5         490         1145      1145      1053  ...         192           84   \n",
       "\n",
       "    EnclosedPorch  3SsnPorch  ScreenPorch  PoolArea  MiscVal  MoSold  YrSold  \\\n",
       "Id                                                                             \n",
       "1               0          0            0         0        0       2    2008   \n",
       "2               0          0            0         0        0       5    2007   \n",
       "3               0          0            0         0        0       9    2008   \n",
       "4             272          0            0         0        0       2    2006   \n",
       "5               0          0            0         0        0      12    2008   \n",
       "\n",
       "    SalePrice  \n",
       "Id             \n",
       "1      208500  \n",
       "2      181500  \n",
       "3      223500  \n",
       "4      140000  \n",
       "5      250000  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('train.csv', index_col=0, usecols=cols)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проверка на пропуски в данных (nan):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'data' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-5ca3891a64dc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'data' is not defined"
     ]
    }
   ],
   "source": [
    "data.isna().sum().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Определяем модель"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.drop('SalePrice', axis=1)\n",
    "Y = data.SalePrice\n",
    "#add the constant term to the data\n",
    "X = sm.add_constant(X)\n",
    "#define the model\n",
    "model = sm.OLS(Y, X)\n",
    "#fit the model\n",
    "results = model.fit()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Вычисляем некоторые статистические параметры:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " the number of paraters to estimate: 31 \n",
      " total number of observations: 1460 \n",
      " degrees of freedom of the model: 30 \n",
      " degrees of freedom of the errors: 1429\n"
     ]
    }
   ],
   "source": [
    "#together with the intercept\n",
    "k = X.shape[1]\n",
    "#total number of observations\n",
    "n = X.shape[0]\n",
    "#degrees of freedom for the model:\n",
    "df_model = k - 1\n",
    "#degrees of freedom of the error:\n",
    "df_error = n - k\n",
    "print(' the number of paraters to estimate: {} \\n total number of observations: {} \\n degrees of freedom of the model: {} \\n degrees of freedom of the errors: {}'\\\n",
    "      .format(k, n, df_model, df_error))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Посчитаем ранг матрицы наблюдений Х:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'np' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-d373a4a460e5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinalg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatrix_rank\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'np' is not defined"
     ]
    }
   ],
   "source": [
    "np.linalg.matrix_rank(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td>Model:</td>               <td>OLS</td>         <td>Adj. R-squared:</td>      <td>0.804</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Dependent Variable:</td>     <td>SalePrice</td>           <td>AIC:</td>         <td>34737.9309</td>\n",
       "</tr>\n",
       "<tr>\n",
       "         <td>Date:</td>        <td>2019-04-10 20:03</td>        <td>BIC:</td>         <td>34896.5166</td>\n",
       "</tr>\n",
       "<tr>\n",
       "   <td>No. Observations:</td>        <td>1460</td>         <td>Log-Likelihood:</td>     <td>-17339.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "       <td>Df Model:</td>             <td>29</td>           <td>F-statistic:</td>        <td>207.6</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Df Residuals:</td>          <td>1430</td>       <td>Prob (F-statistic):</td>    <td>0.00</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "      <td>R-squared:</td>            <td>0.808</td>            <td>Scale:</td>        <td>1.2357e+09</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td></td>           <th>Coef.</th>      <th>Std.Err.</th>      <th>t</th>     <th>P>|t|</th>    <th>[0.025</th>        <th>0.975]</th>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>         <td>608246.1995</td> <td>1428003.7488</td> <td>0.4259</td>  <td>0.6702</td> <td>-2192960.6498</td> <td>3409453.0488</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MSSubClass</th>     <td>-161.5055</td>     <td>26.4469</td>   <td>-6.1068</td> <td>0.0000</td>   <td>-213.3845</td>     <td>-109.6266</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>LotArea</th>         <td>0.3868</td>       <td>0.1016</td>    <td>3.8079</td>  <td>0.0001</td>    <td>0.1875</td>        <td>0.5860</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>OverallQual</th>   <td>18012.7658</td>    <td>1194.9364</td>  <td>15.0742</td> <td>0.0000</td>  <td>15668.7495</td>    <td>20356.7821</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>OverallCond</th>    <td>4425.5378</td>    <td>1031.1020</td>  <td>4.2920</td>  <td>0.0000</td>   <td>2402.9031</td>     <td>6448.1725</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>YearBuilt</th>      <td>349.4880</td>      <td>61.2239</td>   <td>5.7084</td>  <td>0.0000</td>   <td>229.3896</td>      <td>469.5863</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>YearRemodAdd</th>   <td>140.9941</td>      <td>66.3557</td>   <td>2.1248</td>  <td>0.0338</td>    <td>10.8292</td>      <td>271.1590</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>BsmtUnfSF</th>       <td>-9.7412</td>      <td>3.1432</td>    <td>-3.0991</td> <td>0.0020</td>   <td>-15.9070</td>       <td>-3.5754</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TotalBsmtSF</th>     <td>20.5078</td>      <td>4.5849</td>    <td>4.4729</td>  <td>0.0000</td>    <td>11.5140</td>       <td>29.5016</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>1stFlrSF</th>        <td>18.9417</td>      <td>6.1645</td>    <td>3.0727</td>  <td>0.0022</td>    <td>6.8492</td>        <td>31.0341</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>2ndFlrSF</th>        <td>19.5079</td>      <td>5.7011</td>    <td>3.4218</td>  <td>0.0006</td>    <td>8.3244</td>        <td>30.6914</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>LowQualFinSF</th>    <td>-6.7807</td>      <td>14.8869</td>   <td>-0.4555</td> <td>0.6488</td>   <td>-35.9831</td>       <td>22.4218</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>GrLivArea</th>       <td>31.6689</td>      <td>5.7075</td>    <td>5.5486</td>  <td>0.0000</td>    <td>20.4729</td>       <td>42.8650</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>BsmtFullBath</th>   <td>8400.0052</td>    <td>2512.7996</td>  <td>3.3429</td>  <td>0.0009</td>   <td>3470.8365</td>    <td>13329.1738</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>FullBath</th>       <td>3414.1786</td>    <td>2838.1391</td>  <td>1.2030</td>  <td>0.2292</td>  <td>-2153.1840</td>     <td>8981.5413</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>HalfBath</th>      <td>-1549.1048</td>    <td>2690.0026</td>  <td>-0.5759</td> <td>0.5648</td>  <td>-6825.8793</td>     <td>3727.6697</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>BedroomAbvGr</th>  <td>-10546.9569</td>   <td>1704.6005</td>  <td>-6.1873</td> <td>0.0000</td>  <td>-13890.7426</td>   <td>-7203.1712</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>KitchenAbvGr</th>  <td>-12589.7919</td>   <td>5254.5163</td>  <td>-2.3960</td> <td>0.0167</td>  <td>-22897.1787</td>   <td>-2282.4051</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TotRmsAbvGrd</th>   <td>5095.3485</td>    <td>1250.1490</td>  <td>4.0758</td>  <td>0.0000</td>   <td>2643.0258</td>     <td>7547.6713</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Fireplaces</th>     <td>3707.1220</td>    <td>1784.0235</td>  <td>2.0780</td>  <td>0.0379</td>   <td>207.5382</td>      <td>7206.7058</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>GarageCars</th>    <td>10562.3784</td>    <td>2884.3767</td>  <td>3.6619</td>  <td>0.0003</td>   <td>4904.3150</td>    <td>16220.4418</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>GarageArea</th>      <td>2.1462</td>       <td>9.7916</td>    <td>0.2192</td>  <td>0.8265</td>   <td>-17.0613</td>       <td>21.3537</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>WoodDeckSF</th>      <td>25.7208</td>      <td>8.0357</td>    <td>3.2008</td>  <td>0.0014</td>    <td>9.9578</td>        <td>41.4838</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>OpenPorchSF</th>     <td>-6.2873</td>      <td>15.3029</td>   <td>-0.4109</td> <td>0.6812</td>   <td>-36.3058</td>       <td>23.7312</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>EnclosedPorch</th>   <td>6.6916</td>       <td>17.0190</td>   <td>0.3932</td>  <td>0.6942</td>   <td>-26.6932</td>       <td>40.0765</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>3SsnPorch</th>       <td>21.6727</td>      <td>31.6870</td>   <td>0.6840</td>  <td>0.4941</td>   <td>-40.4854</td>       <td>83.8308</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ScreenPorch</th>     <td>55.1441</td>      <td>17.3306</td>   <td>3.1819</td>  <td>0.0015</td>    <td>21.1479</td>       <td>89.1403</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PoolArea</th>       <td>-42.4124</td>      <td>23.8035</td>   <td>-1.7818</td> <td>0.0750</td>   <td>-89.1059</td>       <td>4.2810</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MiscVal</th>         <td>-0.8946</td>      <td>1.8753</td>    <td>-0.4770</td> <td>0.6334</td>    <td>-4.5731</td>       <td>2.7840</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MoSold</th>         <td>-111.5227</td>    <td>348.4163</td>   <td>-0.3201</td> <td>0.7490</td>   <td>-794.9846</td>     <td>571.9391</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>YrSold</th>         <td>-817.2240</td>    <td>709.7266</td>   <td>-1.1515</td> <td>0.2497</td>  <td>-2209.4410</td>     <td>574.9931</td>  \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td>Omnibus:</td>    <td>540.774</td>  <td>Durbin-Watson:</td>         <td>1.961</td>      \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Prob(Omnibus):</td>  <td>0.000</td>  <td>Jarque-Bera (JB):</td>     <td>95836.983</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "       <td>Skew:</td>     <td>-0.553</td>      <td>Prob(JB):</td>           <td>0.000</td>      \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Kurtosis:</td>   <td>42.676</td>   <td>Condition No.:</td>   <td>13129423541445018</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                        Results: Ordinary least squares\n",
       "================================================================================\n",
       "Model:                   OLS                  Adj. R-squared:         0.804     \n",
       "Dependent Variable:      SalePrice            AIC:                    34737.9309\n",
       "Date:                    2019-04-10 20:03     BIC:                    34896.5166\n",
       "No. Observations:        1460                 Log-Likelihood:         -17339.   \n",
       "Df Model:                29                   F-statistic:            207.6     \n",
       "Df Residuals:            1430                 Prob (F-statistic):     0.00      \n",
       "R-squared:               0.808                Scale:                  1.2357e+09\n",
       "--------------------------------------------------------------------------------\n",
       "                 Coef.      Std.Err.      t    P>|t|      [0.025       0.975]   \n",
       "--------------------------------------------------------------------------------\n",
       "const         608246.1995 1428003.7488  0.4259 0.6702 -2192960.6498 3409453.0488\n",
       "MSSubClass      -161.5055      26.4469 -6.1068 0.0000     -213.3845    -109.6266\n",
       "LotArea            0.3868       0.1016  3.8079 0.0001        0.1875       0.5860\n",
       "OverallQual    18012.7658    1194.9364 15.0742 0.0000    15668.7495   20356.7821\n",
       "OverallCond     4425.5378    1031.1020  4.2920 0.0000     2402.9031    6448.1725\n",
       "YearBuilt        349.4880      61.2239  5.7084 0.0000      229.3896     469.5863\n",
       "YearRemodAdd     140.9941      66.3557  2.1248 0.0338       10.8292     271.1590\n",
       "BsmtUnfSF         -9.7412       3.1432 -3.0991 0.0020      -15.9070      -3.5754\n",
       "TotalBsmtSF       20.5078       4.5849  4.4729 0.0000       11.5140      29.5016\n",
       "1stFlrSF          18.9417       6.1645  3.0727 0.0022        6.8492      31.0341\n",
       "2ndFlrSF          19.5079       5.7011  3.4218 0.0006        8.3244      30.6914\n",
       "LowQualFinSF      -6.7807      14.8869 -0.4555 0.6488      -35.9831      22.4218\n",
       "GrLivArea         31.6689       5.7075  5.5486 0.0000       20.4729      42.8650\n",
       "BsmtFullBath    8400.0052    2512.7996  3.3429 0.0009     3470.8365   13329.1738\n",
       "FullBath        3414.1786    2838.1391  1.2030 0.2292    -2153.1840    8981.5413\n",
       "HalfBath       -1549.1048    2690.0026 -0.5759 0.5648    -6825.8793    3727.6697\n",
       "BedroomAbvGr  -10546.9569    1704.6005 -6.1873 0.0000   -13890.7426   -7203.1712\n",
       "KitchenAbvGr  -12589.7919    5254.5163 -2.3960 0.0167   -22897.1787   -2282.4051\n",
       "TotRmsAbvGrd    5095.3485    1250.1490  4.0758 0.0000     2643.0258    7547.6713\n",
       "Fireplaces      3707.1220    1784.0235  2.0780 0.0379      207.5382    7206.7058\n",
       "GarageCars     10562.3784    2884.3767  3.6619 0.0003     4904.3150   16220.4418\n",
       "GarageArea         2.1462       9.7916  0.2192 0.8265      -17.0613      21.3537\n",
       "WoodDeckSF        25.7208       8.0357  3.2008 0.0014        9.9578      41.4838\n",
       "OpenPorchSF       -6.2873      15.3029 -0.4109 0.6812      -36.3058      23.7312\n",
       "EnclosedPorch      6.6916      17.0190  0.3932 0.6942      -26.6932      40.0765\n",
       "3SsnPorch         21.6727      31.6870  0.6840 0.4941      -40.4854      83.8308\n",
       "ScreenPorch       55.1441      17.3306  3.1819 0.0015       21.1479      89.1403\n",
       "PoolArea         -42.4124      23.8035 -1.7818 0.0750      -89.1059       4.2810\n",
       "MiscVal           -0.8946       1.8753 -0.4770 0.6334       -4.5731       2.7840\n",
       "MoSold          -111.5227     348.4163 -0.3201 0.7490     -794.9846     571.9391\n",
       "YrSold          -817.2240     709.7266 -1.1515 0.2497    -2209.4410     574.9931\n",
       "--------------------------------------------------------------------------------\n",
       "Omnibus:               540.774        Durbin-Watson:           1.961            \n",
       "Prob(Omnibus):         0.000          Jarque-Bera (JB):        95836.983        \n",
       "Skew:                  -0.553         Prob(JB):                0.000            \n",
       "Kurtosis:              42.676         Condition No.:           13129423541445018\n",
       "================================================================================\n",
       "* The condition number is large (1e+16). This might indicate             strong\n",
       "multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.summary2()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Объяснение таблички с результатами:**\n",
    "\n",
    "* $R^2 = 0.808$\n",
    "* $R^2_{adj} = 0.804$\n",
    "* Log-Likelihood = -1733 - Значение функции правдоподобия в оптимальной точке\n",
    "* AIC = 34738 - Akaike information criterion, используется для выбора наилучшей модели. Лучше та модель, у которой AIC меньше\n",
    "* BIC = 34897 - Bayesian information criterion, цель та же, что и у AIC\n",
    "* F-statistic = 208\n",
    "\n",
    "**F-test, тест на значимость модели в целом**:\n",
    "\n",
    "$H_{0}$ : Коэффициенты при всех признаках, кроме константы, одновременно равны нулю\n",
    "\n",
    "$H_{1}$ : Не равны нулю, т.е. признаки добавляют информации в модель\n",
    "\n",
    "$F = \\frac{R^2/(k-1)}{(1-R^2)/(n-k)}$,\n",
    "\n",
    "где $k$ - кол-во переменных (вместе с константой), $n$ - кол-во наблюдений\n",
    "\n",
    "\n",
    "Если посчитанное значение статистики F-value больше, чем табличное значение, то мы можем отклонить нулевую гипотезу $H_{0}$\n",
    "\n",
    "* Prob(F-statistic) = 0.0 - P-value for F-test\n",
    "* Df model - degrees of freedom of the model\n",
    "* Df Residuals - degrees of freedom of the errors\n",
    "\n",
    "**Note:** значения Df отличаются (!). Верные значения посчитаны выше. В этой специфической реализации OLS, DF_model считается как ранг матрицы X, который равен 29\n",
    "\n",
    "* Scale - squared standard error (SSE) регрессии\n",
    "\n",
    "* Durbin-Watson = 1.96; DW - тест на автокорреляцию ошибок. Значения DW всегда лежат между 0 и 4. Если DW << 2, то присутствует положительная корреляция, если DW >> 2, то отрицательная корреляция\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** Не все коэффициенты значимы. Что делать? Существует несколько методов (a.k.a. Feature engineering):\n",
    "\n",
    "* **Исключение по P-value:**\n",
    "\n",
    "Строим модель, используя полный набор признаков. Затем, исключаем незначимые признаки итеративно, начиная с того, у которого P-value больше всех\n",
    "\n",
    "* **Forward elimination:**\n",
    "\n",
    "Строим все возможные регрессионные модели с одним признаком и выбираем наилучший. Затем, строим все возможные модели с наилучшим предиктором и еще одним (все возможные двойки). Продолжаем добавлять по одному признаку. Останавливаемся, когда качество модели перестает улучшаться или начинает ухудшаться.\n",
    "\n",
    "* **Backward elimination:**\n",
    "\n",
    "Строим регрессионную модель на всех признаках. Затем, удаляем по одному признаку итеративно (удаляется тот, чье удаление приводит к наибольшему улучшению качества модели). Останавливаемся, когда удаление признаков начинает приводить к ухудшению качества модели."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 1: \n",
    "\n",
    "* Найдите коллинеарные признаки и оставьте только один из них"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 2: Имплементируйте алгоритм исключения признаков по P-value\n",
    "\n",
    "**Hints**:\n",
    "    * используйте циклы\n",
    "    * мониторьте целевую метрику (R^2 adjusted) и P-values соответствующих коэффициентов\n",
    "    * константа должна быть включена в модель\n",
    "    \n",
    "**Note:** Незначимый коэффициент при переменной в одной модели может стать значимым в другой и наоборот \n",
    "**Note 2:** Две модели с разными признаками считаются разными моделями (!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols2 = ['MSSubClass', 'LotArea', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd', 'BsmtUnfSF',\\\n",
    "         'TotalBsmtSF', '1stFlrSF', '2ndFlrSF', 'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'FullBath', 'HalfBath',\\\n",
    " 'BedroomAbvGr', 'KitchenAbvGr', 'TotRmsAbvGrd', 'Fireplaces', 'GarageCars', 'GarageArea', 'WoodDeckSF', 'OpenPorchSF',\\\n",
    " 'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'MiscVal', 'MoSold', 'YrSold']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Logistic Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Подгружаем датасет:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length</th>\n",
       "      <th>sepal width</th>\n",
       "      <th>petal length</th>\n",
       "      <th>petal width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length  sepal width  petal length  petal width\n",
       "0           5.1          3.5           1.4          0.2\n",
       "1           4.9          3.0           1.4          0.2\n",
       "2           4.7          3.2           1.3          0.2\n",
       "3           4.6          3.1           1.5          0.2\n",
       "4           5.0          3.6           1.4          0.2"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X, Y = sklearn.datasets.load_iris(return_X_y=True)\n",
    "names = ['sepal length', 'sepal width', 'petal length', 'petal width']\n",
    "classes = ['setosa', 'versicolor', 'virginica']\n",
    "#create pandas object\n",
    "X = pd.DataFrame(X, columns=names)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Целевая переменная уже закодирована:\n",
    "    * 0 - Setosa\n",
    "    * 1 - Versicolor\n",
    "    * 2 - Verginica\n",
    "    \n",
    "<img style=\"float: left;\" src=\"iris.jpg\" br>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Описательные статистики:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length</th>\n",
       "      <th>sepal width</th>\n",
       "      <th>petal length</th>\n",
       "      <th>petal width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.057333</td>\n",
       "      <td>3.758000</td>\n",
       "      <td>1.199333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.435866</td>\n",
       "      <td>1.765298</td>\n",
       "      <td>0.762238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sepal length  sepal width  petal length  petal width\n",
       "count    150.000000   150.000000    150.000000   150.000000\n",
       "mean       5.843333     3.057333      3.758000     1.199333\n",
       "std        0.828066     0.435866      1.765298     0.762238\n",
       "min        4.300000     2.000000      1.000000     0.100000\n",
       "25%        5.100000     2.800000      1.600000     0.300000\n",
       "50%        5.800000     3.000000      4.350000     1.300000\n",
       "75%        6.400000     3.300000      5.100000     1.800000\n",
       "max        7.900000     4.400000      6.900000     2.500000"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21580054e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21580091160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x215fcc5a748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21580005588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for name in names:\n",
    "    X[name].hist(bins=9, label=name)\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Корреляция:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e6697ff3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt=pd.concat([X,pd.DataFrame(Y, columns=['target'])], axis=1)\n",
    "sns.set(style=\"white\")\n",
    "\n",
    "# Compute the correlation matrix\n",
    "corr = dt.corr()\n",
    "\n",
    "# Generate a mask for the upper triangle\n",
    "mask = np.zeros_like(corr, dtype=np.bool)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "\n",
    "# Set up the matplotlib figure\n",
    "f, ax = plt.subplots(figsize=(11, 9))\n",
    "\n",
    "# Generate a custom diverging colormap\n",
    "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
    "\n",
    "# Draw the heatmap with the mask and correct aspect ratio\n",
    "\n",
    "sns.heatmap(corr, mask=mask, cmap=cmap, center=0,\n",
    "            square=True, linewidths=.5, cbar_kws={\"shrink\": .5})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Еще несколько красивых графиков:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e668f7bb38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(dt, kind='scatter', hue='target')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x215fd2f7a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot( x=dt[\"target\"], y=dt[\"sepal length\"])\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Считаем ранг матрицы признаков:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linalg.matrix_rank(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: Maximum number of iterations has been exceeded.\n",
      "         Current function value: 0.072266\n",
      "         Iterations: 35\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9666666666666667"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#from sklearn import metrics \n",
    "logit = sm.MNLogit(Y, X)\n",
    "result = logit.fit()\n",
    "preds=np.argmax(result.predict(X).values,axis=1)\n",
    "accuracy_score(Y,preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Logistic regression with L1 regularization**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.98"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "model=sklearn.linear_model.LogisticRegression(penalty='l1',multi_class='multinomial', solver='saga').fit(X,Y)\n",
    "preds=model.predict(X)\n",
    "accuracy_score(Y, preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Logistic regression with L2 regularization**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9866666666666667"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "model=sklearn.linear_model.LogisticRegression(penalty='l2',multi_class='multinomial', solver='saga', fit_intercept=True).fit(X,Y)\n",
    "preds=model.predict(X)\n",
    "accuracy_score(Y, preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "newton-cg 0.9733333333333334\n",
      "sag 0.9866666666666667\n",
      "saga 0.9866666666666667\n",
      "lbfgs 0.9733333333333334\n"
     ]
    }
   ],
   "source": [
    "solvers=['newton-cg', 'sag', 'saga', 'lbfgs']\n",
    "for solver in solvers:\n",
    "    model=sklearn.linear_model.LogisticRegression(penalty='l2',multi_class='multinomial', solver=solver, fit_intercept=True).fit(X,Y)\n",
    "    preds=model.predict(X)\n",
    "    print(solver, accuracy_score(Y, preds))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Анализируем результаты"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Визуальный анализ**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 3: \n",
    "* постройте точечные диаграммы (scatter plots) любых двух признаков из X\n",
    "    * a) используйте настоящие значения целевой переменной в качестве цветовой разметки\n",
    "    * b) используйте предсказанные значения в качестве цветовой разметки\n",
    "    \n",
    "**Hint:** используйте sns.lmplot  \n",
    "\n",
    "* найдите как минимум две неправильно классифицированные точки"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Confusion matrix**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 4:\n",
    "* прочитайте про confusion matrix (https://en.wikipedia.org/wiki/Confusion_matrix)\n",
    "* посчитайте и нарисуйте ее (https://scikit-learn.org/0.20/auto_examples/model_selection/plot_confusion_matrix.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 5: Постройте (любую) модель логистической регрессии для двух классов: versicolor и virginica\n",
    "* 0) постройте график парных зависимостей и сделайте цветовую разметку по значениям целевой переменной\n",
    "* a) сформируйте новый датасет, соответствующий поставленной задаче (удалите все, что относится к классу setosa)\n",
    "* b) постройте модель\n",
    "* c) выведите на экран accuracy score"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
